Interface GPUOptionsOrBuilder

All Superinterfaces:
MessageLiteOrBuilder, MessageOrBuilder
All Known Implementing Classes:
GPUOptions, GPUOptions.Builder
public interface GPUOptionsOrBuilder extends MessageOrBuilder

  • Method Details

    • getPerProcessGpuMemoryFraction

      double getPerProcessGpuMemoryFraction()
      Fraction of the total GPU memory to allocate for each process.
1 means to allocate all of the GPU memory, 0.5 means the process
allocates up to ~50% of the total GPU memory.
GPU memory is pre-allocated unless the allow_growth option is enabled.
If greater than 1.0, uses CUDA unified memory to potentially oversubscribe
the amount of memory available on the GPU device by using host memory as a
swap space. Accessing memory not available on the device will be
significantly slower as that would require memory transfer between the host
and the device. Options to reduce the memory requirement should be
considered before enabling this option as this may come with a negative
performance impact. Oversubscription using the unified memory requires
Pascal class or newer GPUs and it is currently only supported on the Linux
operating system. See
https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html#um-requirements
for the detailed requirements.
      double per_process_gpu_memory_fraction = 1;
      Returns:
      The perProcessGpuMemoryFraction.

    • getAllowGrowth

      boolean getAllowGrowth()
      If true, the allocator does not pre-allocate the entire specified
GPU memory region, instead starting small and growing as needed.
      bool allow_growth = 4;
      Returns:
      The allowGrowth.

    • getAllocatorType

      String getAllocatorType()
      The type of GPU allocation strategy to use.
Allowed values:
"": The empty string (default) uses a system-chosen default
    which may change over time.
"BFC": A "Best-fit with coalescing" algorithm, simplified from a
       version of dlmalloc.
      string allocator_type = 2;
      Returns:
      The allocatorType.

    • getAllocatorTypeBytes

      ByteString getAllocatorTypeBytes()
      The type of GPU allocation strategy to use.
Allowed values:
"": The empty string (default) uses a system-chosen default
    which may change over time.
"BFC": A "Best-fit with coalescing" algorithm, simplified from a
       version of dlmalloc.
      string allocator_type = 2;
      Returns:
      The bytes for allocatorType.

    • getDeferredDeletionBytes

      long getDeferredDeletionBytes()
      Delay deletion of up to this many bytes to reduce the number of
interactions with gpu driver code.  If 0, the system chooses
a reasonable default (several MBs).
      int64 deferred_deletion_bytes = 3;
      Returns:
      The deferredDeletionBytes.

    • getVisibleDeviceList

      String getVisibleDeviceList()
      A comma-separated list of GPU ids that determines the 'visible'
to 'virtual' mapping of GPU devices.  For example, if TensorFlow
can see 8 GPU devices in the process, and one wanted to map
visible GPU devices 5 and 3 as "/device:GPU:0", and "/device:GPU:1",
then one would specify this field as "5,3".  This field is similar in
spirit to the CUDA_VISIBLE_DEVICES environment variable, except
it applies to the visible GPU devices in the process.
NOTE:
1. The GPU driver provides the process with the visible GPUs
   in an order which is not guaranteed to have any correlation to
   the *physical* GPU id in the machine.  This field is used for
   remapping "visible" to "virtual", which means this operates only
   after the process starts.  Users are required to use vendor
   specific mechanisms (e.g., CUDA_VISIBLE_DEVICES) to control the
   physical to visible device mapping prior to invoking TensorFlow.
2. In the code, the ids in this list are also called "platform GPU id"s,
   and the 'virtual' ids of GPU devices (i.e. the ids in the device
   name "/device:GPU:<id>") are also called "TF GPU id"s. Please
   refer to third_party/tensorflow/core/common_runtime/gpu/gpu_id.h
   for more information.
      string visible_device_list = 5;
      Returns:
      The visibleDeviceList.

    • getVisibleDeviceListBytes

      ByteString getVisibleDeviceListBytes()
      A comma-separated list of GPU ids that determines the 'visible'
to 'virtual' mapping of GPU devices.  For example, if TensorFlow
can see 8 GPU devices in the process, and one wanted to map
visible GPU devices 5 and 3 as "/device:GPU:0", and "/device:GPU:1",
then one would specify this field as "5,3".  This field is similar in
spirit to the CUDA_VISIBLE_DEVICES environment variable, except
it applies to the visible GPU devices in the process.
NOTE:
1. The GPU driver provides the process with the visible GPUs
   in an order which is not guaranteed to have any correlation to
   the *physical* GPU id in the machine.  This field is used for
   remapping "visible" to "virtual", which means this operates only
   after the process starts.  Users are required to use vendor
   specific mechanisms (e.g., CUDA_VISIBLE_DEVICES) to control the
   physical to visible device mapping prior to invoking TensorFlow.
2. In the code, the ids in this list are also called "platform GPU id"s,
   and the 'virtual' ids of GPU devices (i.e. the ids in the device
   name "/device:GPU:<id>") are also called "TF GPU id"s. Please
   refer to third_party/tensorflow/core/common_runtime/gpu/gpu_id.h
   for more information.
      string visible_device_list = 5;
      Returns:
      The bytes for visibleDeviceList.

    • getPollingActiveDelayUsecs

      int getPollingActiveDelayUsecs()
      In the event polling loop sleep this many microseconds between
PollEvents calls, when the queue is not empty.  If value is not
set or set to 0, gets set to a non-zero default.
      int32 polling_active_delay_usecs = 6;
      Returns:
      The pollingActiveDelayUsecs.

    • getPollingInactiveDelayMsecs

      int getPollingInactiveDelayMsecs()
      This field is deprecated and ignored.
      int32 polling_inactive_delay_msecs = 7;
      Returns:
      The pollingInactiveDelayMsecs.

    • getForceGpuCompatible

      boolean getForceGpuCompatible()
      Force all tensors to be gpu_compatible. On a GPU-enabled TensorFlow,
enabling this option forces all CPU tensors to be allocated with Cuda
pinned memory. Normally, TensorFlow will infer which tensors should be
allocated as the pinned memory. But in case where the inference is
incomplete, this option can significantly speed up the cross-device memory
copy performance as long as it fits the memory.
Note that this option is not something that should be
enabled by default for unknown or very large models, since all Cuda pinned
memory is unpageable, having too much pinned memory might negatively impact
the overall host system performance.
      bool force_gpu_compatible = 8;
      Returns:
      The forceGpuCompatible.

    • hasExperimental

      boolean hasExperimental()
      Everything inside experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
      .tensorflow.GPUOptions.Experimental experimental = 9;
      Returns:
      Whether the experimental field is set.

    • getExperimental

      GPUOptions.Experimental getExperimental()
      Everything inside experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
      .tensorflow.GPUOptions.Experimental experimental = 9;
      Returns:
      The experimental.

    • getExperimentalOrBuilder

      GPUOptions.ExperimentalOrBuilder getExperimentalOrBuilder()
      Everything inside experimental is subject to change and is not subject
to API stability guarantees in
https://www.tensorflow.org/guide/version_compat.
      .tensorflow.GPUOptions.Experimental experimental = 9;